Permanent magnet demagnetization fault identification method, system and device and medium
By introducing an exponential band drift Wiener process and an Arennis acceleration model, real-time monitoring of permanent magnet demagnetization faults has been solved, and the problem of real-time monitoring and calculation complexity in the existing technology has been solved, achieving more accurate failure rate prediction and scientific maintenance of motor equipment.
Patent Information
- Application Number
- CN202510785490.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-12
- Publication Date
- 2025-09-02
AI Technical Summary
The prior art cannot monitor permanent magnet demagnetization faults in real time, and the calculation complexity is high in scenarios where large amounts of data are processed or real-time requirements are high, resulting in inaccurate prediction of failure rate.
The exponential band drifting Wiener process is combined with the Arennis acceleration model, and the magnetic field intensity data on the surface of the permanent magnet is measured regularly, and the fitting curve is generated and an exponential band drifting Wiener process is introduced to construct a permanent magnet surface magnetic field intensity model, and parameter estimation and demagnetization failure rate calculation are carried out.
It realizes real-time monitoring of permanent magnet demagnetization faults during motor operation, improves the accuracy and real-timeness of failure rate prediction, overcomes the shortcomings of the deterministic degradation assumption in traditional methods, and provides a more scientific maintenance strategy.
Smart Images
Figure CN120579339A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of permanent magnet demagnetization fault identification, and in particular to a permanent magnet demagnetization fault identification method, system, equipment and medium. Background Art
[0002] The existing permanent magnet demagnetization method is to establish a PMSLM local demagnetization analytical model based on the equivalent magnetization intensity method. After making assumptions such as the existence of only XY two-dimensional magnetic fields, infinite secondary iron yoke permeability, and uniform magnetization of the permanent magnet, the formulas for the air gap region magnetic flux density and the a-phase induced electromotive force are obtained. By calculating the absolute value of the difference between the induced electromotive force in the demagnetized state and the normal state, the demagnetization method is used to determine the demagnetization state. , deriving a multi-peak graph as the basis for fault feature analysis. Different demagnetization fault types are then analyzed, and multi-peak graph features are extracted to accurately distinguish different demagnetization faults. A classification model is constructed using the PSOLSSVM algorithm to identify localized demagnetization faults in permanent magnets.
[0003] However, this method suffers from limitations such as offline detection, fault simulation limitations, the influence of assumptions, computational complexity, and limited sample size, which limit its use. This method relies on offline detection methods such as finite element simulation and prototype testing, making it incapable of real-time monitoring of permanent magnet demagnetization faults during motor operation. Furthermore, while the PSOLSSVM algorithm improves classification accuracy, the PSO algorithm increases computational complexity during the LSSVM parameter optimization process. This may not meet the requirements for rapid processing and real-time decision-making when processing large amounts of data or in scenarios with high real-time requirements. This presents significant deficiencies for applications requiring real-time monitoring of motor operating status, prompt fault detection, and immediate resolution. Summary of the Invention
[0004] In order to solve the above technical problems, the present invention proposes a permanent magnet demagnetization fault identification method, system, equipment and medium, aiming to provide a permanent magnet demagnetization failure rate acquisition method that combines performance degradation data with the Wiener process. By introducing an exponential drift Wiener process to describe the degradation trend and random fluctuations of the magnetic field strength on the surface of the permanent magnet, and using the Arrhenius acceleration model to quantify the impact of temperature on the degradation rate, the problem of inaccurate failure rate prediction caused by the failure to fully consider the randomness of the degradation process and environmental factors (such as temperature) in existing methods is solved.
[0005] In order to achieve the above object, the present invention provides the following solutions: In a first aspect, the present invention provides a method for identifying a permanent magnet demagnetization fault, the method comprising the following steps: Multiple initial permanent magnets of the same parameter type are magnetized and grouped, then placed in a constant temperature chamber with different temperature environments for acceleration based on the Arrhenius acceleration model. The surface magnetic field strength data of the permanent magnets is measured and recorded at regular intervals, and a fitting curve is generated after a time threshold is met. generating a degradation function based on the fitting curve, and introducing an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; Parameters of the permanent magnet surface magnetic field strength model are estimated, and a demagnetization failure rate is generated under a set demagnetization failure threshold, and demagnetization failure identification is performed based on the demagnetization failure rate.
[0006] Furthermore, the degradation function is expressed as follows: ; in, is the initial magnetic field brightness at different temperatures, is the degradation rate at different temperatures.
[0007] Furthermore, the magnetic field strength model of the permanent magnet surface is expressed by the following formula: ; in, is the temperature-dependent fluctuation rate, is a standard Wiener process.
[0008] Furthermore, the process of estimating parameters of the permanent magnet surface magnetic field strength model is specifically as follows: Generating a k value with the minimum fitting error based on least squares fitting according to the multiple sets of degradation data corresponding to the fitting curve; Calculate the exponential trend data at each time point t corresponding to multiple sets of degradation data ; The detrended data sequence is obtained by subtracting the exponential trend data from the measured value in the fitting curve And the difference between adjacent time points: ; ; in, ; Calculate the degradation data of each group Variance Then we get the parameter estimation results : ; in, is the increment of the Wiener process, which obeys the normal distribution , n is the number of groups, M is the number of time points, and k is the degradation rate.
[0009] Furthermore, the step of generating a demagnetization failure rate under the set demagnetization failure threshold includes: Setting the demagnetization fault threshold , according to the demagnetization fault threshold Determine the current state, if , it is in failure state; The Monte Carlo simulation method is used to obtain the time distribution of exceeding the threshold, which is approximately a normal distribution: ; The portion exceeding the demagnetization fault threshold is regarded as a failure, and the time distribution is approximately a lifetime distribution. The probability density function of the lifetime distribution is: ; The cumulative distribution function is expressed as: ; The generated demagnetization failure rate is Indicates that: ; in, Demagnetization fault threshold degradation rate.
[0010] Furthermore, the demagnetization fault threshold The fault threshold is set to 500 Gauss permanent magnet magnetic field strength corresponding to the motor torque dropping to 1 / 3.
[0011] Furthermore, the Arrhenius acceleration model and the acceleration factor AF are respectively expressed as: ; ; k is the degradation rate, A is the frequency factor, Ea is the activation energy, R is the gas constant, T is the absolute temperature, Is the normal operating temperature, is the temperature after acceleration.
[0012] In a second aspect, the present invention further proposes a permanent magnet demagnetization fault identification system for executing the permanent magnet demagnetization fault identification method as described in the first aspect. The permanent magnet demagnetization fault identification system includes: A fitting curve generation module is used to magnetize and group multiple initial permanent magnets of the same parameter type, place them in a constant temperature chamber with different temperature environments, and accelerate them based on the Arrhenius acceleration model. The module generates a fitting curve by regularly measuring and recording the surface magnetic field strength data of the permanent magnets and generating a fitting curve after a time threshold is met. A processing module, configured to generate a degradation function based on the fitting curve, and introduce an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; The identification module is used to estimate parameters of the magnetic field strength model on the surface of the permanent magnet, generate a demagnetization failure rate under the action of a set demagnetization failure threshold, and perform demagnetization fault identification based on the demagnetization failure rate.
[0013] In a third aspect, the present invention further proposes an electronic device comprising a memory and a processor, characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the permanent magnet demagnetization fault identification method described in the first aspect.
[0014] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the permanent magnet demagnetization fault identification method described in the first aspect.
[0015] The present invention proposes a permanent magnet demagnetization fault identification method, system, device and medium, which decomposes the permanent magnet demagnetization process into a deterministic exponential decay trend (consisting of drift coefficients) by using an exponential drift Wiener process. characterized by) and random fluctuations (characterized by volatility Compared with the traditional model based only on deterministic trends, this model can more realistically reflect the random fluctuations caused by factors such as material micro defects and environmental noise in the actual degradation process.
[0016] By combining the stochastic properties of the Wiener process with accelerated experimental data, this method overcomes the flaws of traditional methods that rely on a one-size-fits-all deterministic degradation assumption. For example, at key time points such as 500 and 1000 hours, the degradation distribution is fitted using a normal distribution (with an exponential decrease in the mean and an exponential increase in the variance). This accurately reflects the time-accumulating uncertainty of the degradation process, making the calculation of the failure rate λ(t) (defined as the probability of a surviving item failing instantaneously) more accurate to actual operating conditions. This provides a scientific basis for optimizing maintenance strategies for permanent magnet equipment such as motors. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 This is a flow chart of the permanent magnet demagnetization fault identification method proposed in the present invention.
[0018] Figure 2 This is a schematic diagram of the distribution of degradation data obtained in the permanent magnet demagnetization fault identification method proposed in the present invention.
[0019] Figure 3This is a schematic diagram of the distribution upper limit, mean value and random curve of the 95% confidence interval in the permanent magnet demagnetization fault identification method proposed in the present invention.
[0020] Figure 4 This is a schematic diagram of the time distribution of exceeding the demagnetization fault threshold in the permanent magnet demagnetization fault identification method proposed in the present invention. DETAILED DESCRIPTION
[0021] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.
[0022] In order to obtain the degradation rate of the permanent magnets used in the motor, an estimation method based on performance degradation is used, such as Figure 1 As shown, the permanent magnet demagnetization fault identification method specifically includes the following steps: After magnetizing multiple initial permanent magnets of the same parameter type, they are grouped and placed in a constant temperature box with different temperature environments. They are accelerated based on the Arrhenius acceleration model. The surface magnetic field strength data of the permanent magnets is measured and recorded at regular intervals, and a fitting curve is generated after a time threshold is met.
[0023] generating a degradation function based on the fitting curve, and introducing an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; Parameters of the permanent magnet surface magnetic field strength model are estimated, and a demagnetization failure rate is generated under a set demagnetization failure threshold, and demagnetization failure identification is performed based on the demagnetization failure rate.
[0024] This embodiment generates a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process by introducing an exponential Wiener process with drift. Therefore, the specific Wiener process is first described as follows: A Wiener process is a continuous-time random process that satisfies the following four conditions: Initial conditions: ; Independent increment: for any point in time , increment , , ... are independent of each other; Smooth increment: Each increment , , ... obey the mean 0 and variance Normal distribution; Continuous path: The path is continuous at every moment, but is almost not differentiable everywhere.
[0025] The exponential degradation process function is: ; Therefore, the expression of the exponential Wiener process with drift is: ; The above content is applied to the specific method of this embodiment. 20 permanent magnets of the same model and batch are magnetized and divided into three groups. The three accelerated test temperatures are set at 100°C, 130°C, and 160°C. An accelerated test is performed based on the Arrhenius acceleration model. The three groups of permanent magnets are placed in a constant temperature box at the corresponding temperature and heated. The magnets are taken out at regular intervals and the fixed-point surface magnetic field strength of the permanent magnets is measured with a gaussmeter for up to 500 hours.
[0026] In this embodiment, the Arrhenius acceleration model and the acceleration factor AF are respectively expressed as: ; ; k is the degradation rate, A is the frequency factor, Ea is the activation energy, R is the gas constant, T is the absolute temperature, Is the normal operating temperature, is the temperature after acceleration.
[0027] The test result data obtained are as follows Figure 2 As shown by Figure 2 It can be seen that the downward trend of the data at each temperature is approximately the same. According to the curve in the figure, a degradation function can be generated as shown below: ; in, is the initial magnetic field brightness at different temperatures, is the degradation rate at different temperatures. By the least square method, we can get , , , substituting the above acceleration factor, we can get It should be noted that compared with the traditional method that relies on long-term natural degradation experiments, this method significantly shortens the experimental cycle. At the same time, by quantifying the impact of temperature on the degradation rate (acceleration factor AF), it improves the universality of failure rate prediction under different working conditions.
[0028] The exponential Wiener process with drift is introduced into the degradation function to construct the magnetic field intensity model of the permanent magnet surface, which is expressed as follows: ; in, is the temperature-dependent fluctuation rate, is a standard Wiener process.
[0029] Parameter estimation is performed for the above process. The specific process is as follows: Generating a k value with the minimum fitting error based on least squares fitting according to the multiple sets of degradation data corresponding to the fitting curve; Calculate the exponential trend data at each time point t corresponding to multiple sets of degradation data ; The detrended data sequence is obtained by subtracting the exponential trend data from the measured value in the fitting curve And the difference between adjacent time points: ; ; in, ; Calculate the degradation data of each group Variance Then we get the parameter estimation results : ; in, is the increment of the Wiener process, which obeys the normal distribution , n is the number of groups, M is the number of time points, and k is the degradation rate.
[0030] according to Figure 2 The content can be obtained , , , we can get , from this we can get the degradation expression of the surface magnetic field strength of the permanent magnet at 40℃: ; From the BT curve we can get G. Based on the above Wiener process, at any time, it obeys the normal distribution: ; like Figure 3 As shown, a 95% confidence interval is proposed, then the upper limit, mean and random curve are as follows Figure 3 shown.
[0031] In this embodiment, the step of generating the demagnetization failure rate under the set demagnetization failure threshold includes: Setting the demagnetization fault threshold , according to the demagnetization fault threshold Determine the current state, if , it is in failure state; The Monte Carlo simulation method is used to obtain the time distribution of exceeding the threshold, which is approximately a normal distribution: ; This process is applied to the calculation of the specific embodiment of the permanent magnet surface magnetic field strength model. The 500 Gauss permanent magnet magnetic field strength corresponding to the motor torque dropping to 1 / 3 is the fault threshold. ,like Figure 4 As shown, the time distribution is approximately normal distribution, that is: .
[0032] The portion exceeding the demagnetization fault threshold is regarded as a failure, and the time distribution is approximately a lifetime distribution. The probability density function of the lifetime distribution is: ; The cumulative distribution function is expressed as: ; The generated demagnetization failure rate is Indicates that: ; in, Demagnetization fault threshold The degradation rate of the permanent magnet is calculated and used for demagnetization fault identification.
[0033] Compared with existing fuzzy threshold judgment methods, this invention uses probabilistic statistics to clarify the failure time distribution at different confidence levels (such as the upper and lower limit curves of the 95% confidence interval). For example, at 40°C, based on the degradation expression, the mean and variance of the magnetic field intensity distribution at any time t can be accurately calculated, providing a quantitative basis for reliability design.
[0034] By combining the random characteristics of the Wiener process with accelerated experimental data, this invention overcomes the shortcomings of the traditional method of "one-size-fits-all" deterministic degradation assumption. For example, at key time points such as 500 hours and 1000 hours, the degradation amount distribution is fitted by normal distribution (mean exponentially decreasing, variance exponentially increasing), accurately reflecting the characteristics of the uncertainty accumulation of the degradation process over time, making the failure rate The calculation (defined as the probability of a surviving item failing instantly) is more in line with actual working conditions and provides a scientific basis for optimizing maintenance strategies for permanent magnet application equipment such as motors.
[0035] In this embodiment, a permanent magnet demagnetization fault identification system is further provided, which is used to perform the permanent magnet demagnetization fault identification method as described in the first aspect. The permanent magnet demagnetization fault identification system includes: A fitting curve generation module is used to magnetize and group multiple initial permanent magnets of the same parameter type, place them in a constant temperature chamber with different temperature environments, and accelerate them based on the Arrhenius acceleration model. The module generates a fitting curve by regularly measuring and recording the surface magnetic field strength data of the permanent magnets and generating a fitting curve after a time threshold is met. A processing module, configured to generate a degradation function based on the fitting curve, and introduce an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; The identification module is used to estimate parameters of the magnetic field strength model on the surface of the permanent magnet, generate a demagnetization failure rate under the action of a set demagnetization failure threshold, and perform demagnetization fault identification based on the demagnetization failure rate.
[0036] This embodiment further provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may invoke logic instructions in the memory to execute the above-described permanent magnet demagnetization fault identification method.
[0037] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0038] This embodiment further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the permanent magnet demagnetization fault identification method provided by the above methods is implemented.
[0039] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.
[0040] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.
[0041] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that variations and improvements are possible without departing from the scope of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.
Claims
1. A method for identifying permanent magnet demagnetization faults, characterized in that: The method comprises the following steps: Multiple initial permanent magnets of the same parameter type are magnetized and grouped, then placed in a constant temperature chamber with different temperature environments for acceleration based on the Arrhenius acceleration model. The surface magnetic field strength data of the permanent magnets is measured and recorded at regular intervals, and a fitting curve is generated after a time threshold is met. generating a degradation function based on the fitting curve, and introducing an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; Parameters of the permanent magnet surface magnetic field strength model are estimated, and a demagnetization failure rate is generated under a set demagnetization failure threshold, and demagnetization failure identification is performed based on the demagnetization failure rate.
2. The permanent magnet demagnetization fault identification method according to claim 1, characterized in that: The degradation function is expressed as follows: ； in, is the initial magnetic field brightness at different temperatures, is the degradation rate at different temperatures.
3. The permanent magnet demagnetization fault identification method according to claim 2, characterized in that: The magnetic field intensity model of the permanent magnet surface is expressed by the following formula: ； in, is the temperature-dependent fluctuation rate, is a standard Wiener process.
4. The permanent magnet demagnetization fault identification method according to claim 3, characterized in that: The process of estimating the parameters of the permanent magnet surface magnetic field strength model is specifically as follows: Generating a k value with the minimum fitting error based on least squares fitting according to the multiple sets of degradation data corresponding to the fitting curve; Calculate the exponential trend data at each time point t corresponding to multiple sets of degradation data ; The detrended data sequence is obtained by subtracting the exponential trend data from the measured value in the fitting curve And the difference between adjacent time points: ； ； in, ； Calculate the degradation data of each group Variance Then we get the parameter estimation results : ； in, is the increment of the Wiener process, which obeys the normal distribution , n is the number of groups, M is the number of time points, and k is the degradation rate.
5. The permanent magnet demagnetization fault identification method according to claim 4, characterized in that: The steps of generating a demagnetization failure rate under a set demagnetization failure threshold include: Setting the demagnetization fault threshold , according to the demagnetization fault threshold Determine the current state, if , it is in failure state; The Monte Carlo simulation method is used to obtain the time distribution of exceeding the threshold, which is approximately a normal distribution: ； The portion exceeding the demagnetization fault threshold is regarded as a failure, and the time distribution is approximately a lifetime distribution. The probability density function of the lifetime distribution is: ； The cumulative distribution function is expressed as: ； The generated demagnetization failure rate is Indicates that: ； in, Demagnetization fault threshold degradation rate.
6. The permanent magnet demagnetization fault identification method according to claim 5, characterized in that: Demagnetization fault threshold The fault threshold is set to 500 Gauss permanent magnet magnetic field strength corresponding to the motor torque dropping to 1 / 3.
7. The method for identifying permanent magnet demagnetization fault according to claim 4, characterized in that: The Arrhenius acceleration model and the acceleration factor AF are respectively expressed as: ； is the degradation rate, A is the frequency factor, Ea is the activation energy, R is the gas constant, T is the absolute temperature, Is the normal operating temperature, is the temperature after acceleration.
8. Permanent magnet demagnetization fault identification system, characterized in that: For executing the permanent magnet demagnetization fault identification method according to any one of claims 1 to 7, the permanent magnet demagnetization fault identification system comprises: A fitting curve generation module is used to magnetize and group multiple initial permanent magnets of the same parameter type, place them in a constant temperature chamber with different temperature environments, and accelerate them based on the Arrhenius acceleration model. The module generates a fitting curve by regularly measuring and recording the surface magnetic field strength data of the permanent magnets and generating a fitting curve after a time threshold is met. A processing module, configured to generate a degradation function based on the fitting curve, and introduce an exponential Wiener process with drift into the degradation function to generate a permanent magnet surface magnetic field intensity model with a fluctuating Wiener process; The identification module is used to estimate parameters of the magnetic field strength model on the surface of the permanent magnet, generate a demagnetization failure rate under the action of a set demagnetization failure threshold, and perform demagnetization fault identification based on the demagnetization failure rate.
9. An electronic device, characterized in that: It includes a memory and a processor, characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the permanent magnet demagnetization fault identification method described in any one of claims 1 to 5.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the permanent magnet demagnetization fault identification method according to any one of claims 1 to 5 is implemented.